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\documentclass[11pt]{article}
\usepackage{acl2013}
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\title{Learning New Named Entity Types by Transfer Learning}

\author{A Anonymous 
   \\%NICTA / Locked Bag 8001, \\ Canberra ACT 2601, Australia \\
   \\ %The Australian National University\\
   \\ %University of Canberra \\
  \\ % {\tt \small{@nicta.com.au}} \\
\And
  B Anonymous
   \\%NICTA / Locked Bag 8001, \\ Canberra ACT 2601, Australia \\
   \\%The Australian National University\\ \\
   \\ %{\tt \small{@nicta.com.au}} \\
}

\date{2015}

% Max 8 pp.

\begin{document}
\maketitle

\begin{abstract}
We present a deep learning based transfer learning model to learn
a set of Named Entity types from a small amount of
training data by leveraging a large training corpus
annotated with some related, but different Named
Entity types.
Our study focus in the main aspects of transfer learning, and in particular 
a in transfer learning between NER systems that have a different label distribution: (i) what to transfer?, (ii) how to transfer?, and (iii) when to transfer?
We found that... 

(i) what to transfer?
feature representation transfer (supervised and unsupervised feature construction) vs. instance transfer vs. transferring knowledge of parameters (prior distribution of parameters) vs. transferring relational knowledge (statistical learning, relational learning techniques)

We transfer feature weights or feature representations
Why we are not targeting other transfer options?

(ii) how to transfer?


and (iii) when to transfer?

Abstract features vs. specific features in a NN


\end{abstract}

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\input{intro}
\input{transferLearning}
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\input{results} 
\input{relwork}
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\section*{Acknowledgments}

Anonymised\\
Anonymised\\
Anonymised\\
Anonymised\\
Anonymised\\
Anonymised\\

%NICTA is funded by the Australian Government as represented by the Department of Broadband, Communications and the Digital Economy and the Australian Research Council through the ICT Centre of Excellence program.

\bibliographystyle{acl2013}
\bibliography{biblio}

\end{document}


